Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/bankstatemently/plugins/analyze-spendingnpx skills add bankstatemently/plugins --skill analyze-spendinggit clone --depth 1 https://github.com/bankstatemently/pluginsWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00048 | $0.00270 |
| Opus 5 | $0.00024 | $0.00135 |
| Sonnet 5 | $0.00010 | $0.00054 |
| Haiku 4.5 | $0.00005 | $0.00027 |
Grade A, and why
analyze-spending scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Analyze
Input: the user's analysis scope — account, product, content hash, date range, or free-text filter. Output: category totals, merchant or counterparty totals, top merchants, and a monthly trend, each row citing content_hash.
Treat the input as the user's analysis scope: account, product, content hash, date range, or free-text filter.
Follow this sequence:
- If the scoped statement has not been categorized, call
categorize_statement. - Call
group_byfor category totals within the scope. - Call
group_byfor merchant or counterparty totals within the scope. - Call
top_nto rank the largest merchants or counterparties within the scope. - Call
time_serieswith monthly buckets for trend figures within the scope. - Present category totals, merchant or counterparty totals, top merchants, and the monthly trend. Cite content_hash for every figure or table row you report.
Example prompt: Categorize my checking account spending last quarter and show top merchants plus the monthly trend.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 23 lines · 48 tokens per session scan A 78de71662aa1
analyze-spending is a skill published in the GitHub repository bankstatemently/plugins (1 stars, last pushed 3d ago), licensed MIT. It adds 48 tokens to every session and 270 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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